Electromagnetic simulations in frequency and time domain using adaptive integral method

ELECTROMAGNETIC SIMULATIONS IN FREQUENCY AND TIME DOMAIN USING ADAPTIVE INTEGRAL METHOD NG TIONG HUAT (M. Eng, B. Eng. (Hons) NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY OF ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE c 2008 Acknowledgements I would like to thank the National University of Singapore for awarding me the postgraduate scholarship to enable me to pursue my studies in microwave communications. I am deeply indebted to Professor Leong Mook Seng, Professor Kooi Pang Shyan and Professor Ooi Ban Leong who taught me much about the fundamentals of computational electromagnetics. Without their kind assistances and patient teaching, the progress of this project would not be possible. I would also like to thank Professor Chew Siou Teck for his advices and providing me with many valuable insights into the techniques of designing microwave circuits. I would also like to thank the staffs from Microwave Laboratory and the Digital Communication Laboratory in the Electrical and Computing Engineering (ECE) department, especially Mr Teo Thiam Chai, Mr Sing Cheng Hiong, Mdm Lee Siew Choo and Mr Jalil for their kind assistances in providing the essential support for the fabrication processes and measurement of the prototypes presented in this thesis. I am also deeply indebted to my fellow team mates from the microwave research laboratory, especially Dr Ewe Weibin, Mr Tham Jingyao, Mr Chua Chee Pargn, Miss Fan Yijin, Dr Sun Jin, Miss Zhang Yaqiong, Miss Irene and Miss Wang Yin for providing the fun, laughter and plentiful of constructive suggestions throughout my post graduate study. I also like to thank my family for their understanding and support, without which this thesis would have been very different. Last but not least, I would like to thank Cindy. She has been my pillar of support and sources of inspirations through all the difficult times. i Table of Contents Acknowledgements i Table of Contents ii Summary v List of Symbols xix Introduction 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Overview of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Original contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Formulation and Numerical Method 2.1 Vector Wave Equation . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Electric Field Integral Equation Formulation for Perfect Electric Conductor Scatterer . . . . . . . . . . . . . . . . . . . . . 2.1.2 Magnetic Field Integral Equation Formulation for Perfect Electric Conductor Scatterer . . . . . . . . . . . . . . . . . . . . . 2.2 Solution using Method of Moments . . . . . . . . . . . . . . . . . . . 2.2.1 Preconditioners for Iterative Solvers . . . . . . . . . . . . . . . 2.2.2 Internal Resonance Problem of EFIE and MFIE . . . . . . . . 2.3 Solving Combined Field Integral Equation using Adaptive Integral Method . . . . . . . . . . . . . . . . . . . . . corr 2.3.1 Near Field Correction Matrix Z . . . . . . . . . . . . . . . 2.3.2 Basis Functions to Grid Sources Projection Schemes . . . . . . 2.4 Proposed New Testing Scheme for MFIE using AIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . 24 29 Interlaced FFT Method for Parallelizing AIM 3.1 Idea and Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Computational Complexity of the New Method . . . . . . . . . . . . 35 36 40 ii 11 12 13 17 17 18 20 21 3.3 3.4 Implementation of Interlaced FFT on Small Cluster of Computer Systems . . . . . . . . . . . . . . . . . . . . . 3.3.1 Allocation of Parallel Computing Resources . . . 3.3.2 Performance Measurement for Parallel Processes . Simulation Results and Dicussions . . . . . . . . . . . . . Distributed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 44 45 46 Efficient Multi-layer Planar Circuit Analysis using Adaptive Integral Method 53 4.1 Multi-Layer Planar Green’s Function . . . . . . . . . . . . . . . . . . 55 4.1.1 Mixed Potential Form of Green’s Function for Planarly Stratified Medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1.2 Numerical Evaluation of Sommerfeld Integrals . . . . . . . . . 60 4.1.3 Infinite Length Transmission Line Problem . . . . . . . . . . . 65 4.1.4 Discrete Complex Image Method . . . . . . . . . . . . . . . . 77 4.2 Simulation of Multi-layer Planar Structures . . . . . . . . . . . . . . 89 4.2.1 De-Embedding of Network Parameters . . . . . . . . . . . . . 89 4.2.2 Evaluating the MoM Matrix for Multi-layer Planar Structures 95 4.2.3 Modeling the Planar Circuit Losses in the Numerical Simulation 98 4.2.4 Vertical Conducting Vias . . . . . . . . . . . . . . . . . . . . . 101 4.2.5 Interpolating Scheme for Green’s Function in multi-layered media103 4.2.6 Microstrip Antenna Pattern . . . . . . . . . . . . . . . . . . . 104 4.3 Numerical Simulation of Ku Band Planar Waveguide to Microstrip Transition by MoM . . . . . . . . . . . . . . 105 4.4 Numerical Simulation of Planar Waveguide Ku Band Power Combiner/Divider circuits Using AIM . . . . . . . . . . . . . . . . . . . . 116 4.5 Effective Simulation of Large Microstrip Circuits . . . . . . . . . . . . 139 4.5.1 Iterative Partial Matrix Solving . . . . . . . . . . . . . . . . . 140 4.5.2 Implementation of Partial Matrix Solving using AIM . . . . . 142 4.5.3 Parallel Block ILU . . . . . . . . . . . . . . . . . . . . . . . . 149 4.5.4 Numerical Results of Parallel PMS-AIM Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Time Domain Integral Equation 5.1 Time Domain Integral Equation Formulation . . . . . . . . . . . . . . 5.2 Far Field Scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Evaluation of TD-AIM using Multi-level Block Space-Time FFT . . 5.4 Alternative Scheme for Block Aggregate Matrix-Vector Multiply . . . 5.4.1 Level and Choosing the Smallest Elementary Block Aggregate Matrix for Level . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Level and Choosing the Smallest Elementary Block Aggregate Matrix for Level . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Generalization to Level and Higher Levels . . . . . . . . . . 5.5 Experimental Determination of the Speed-Up Factor . . . . . . . . . iii 196 198 204 206 213 215 218 225 230 5.6 5.7 5.8 5.9 Implementation of the New Scheme . . Memory Storage and the Complexity of Parallelization of the Computation . . Numerical Results and Discussions . . . . . . . . . . . . the Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 238 240 244 Conclusions 268 Bibliography 271 A Mixed Potential form of Dyadic Green’s Function For Planarly Stratified Medium 285 B Preconditioning of the MoM Matrices B.1 Diagonal Preconditioner . . . . . . . . . . . . B.2 Block Preconditioner . . . . . . . . . . . . . . B.3 Incomplete LU Decomposition method ILU(0) B.4 ILUT . . . . . . . . . . . . . . . . . . . . . . B.5 Block ILU . . . . . . . . . . . . . . . . . . . . iv . . . . [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 290 290 291 292 293 Summary The subject of this thesis is to investigate methods to improve the performances of the adaptive integral method (AIM ) for effective large scale simulations in both the frequency and the time domain. This is achieved by reducing the storage requirements, decreasing the amount of computational loads and implementing effective parallelization strategies. In AIM, the potentials on the auxiliary grid are computed by the convolution of the nodal grid currents with the discrete Green’s function. Using the method of moments (MoM ), the nodal potentials are then interpolated onto the testing functions on the surface of the scatterer and appropriate boundary conditions are then enforced. The Galerkin’s method uses the same set of basis functions as the testing functions. Using the Galerkin’s method, the testing procedure for the electric field integral equation (EFIE ) can be obtained by multiplying the multipole coefficients of the testing functions with their respective nodal potentials. The testing method with magnetic field integral equation (MFIE ) is more elaborated as it involves the cross product with the surface normals of the testing functions and the curl of the nodal potentials. By weighting the contributions of the surface normals corresponding to each pair of triangular basis function, it is possible to use the same multipole coefficients of the testing functions to perform the testings for MFIE. Using the Galekin’s method, the proposed approximation eradicates the need to store any extra interpolation coefficients for MFIE testing separately and enables the combined field integral equation (CFIE ) to be evaluated using less memory resources. Numerical results have shown v that the new testing scheme is as accurate as the conventional methods. Due to the nature of convolution, the nodal potentials are smoother functions spatially as compared to the nodal sources. As such, they can be evaluated at wider grid spaces. A newly proposed method uses interlace grids to compute the nodal potentials effectively. The current sources are first projected onto the AIM auxiliary grids. By choosing every alternate node in the x direction, the original grid can be separated into two independent grids of twice the original spacing in x direction. Similar separation of the nodal grid can be applied to the y and z directions to obtain a maximum of independent grids that have twice the spacing in each of the x, y and z direction. The potentials can then be obtained by convolving the discrete Green’s function with the nodal currents on all the independent auxiliary grids, which can be handled by independent processors. Lagrange interpolation is used to compute all the potentials at original grid points. The contribution of all the grids nodal potentials are then summed to obtain the total contribution by all the sources. This scheme is used to parallelize the computation of AIM to run on a small cluster of parallel computers and the results show that good parallelism is achieved. For microstrip circuits, the coupling potential decays rapidly with increasing distance from the source point. As such, the far couplings between source basis functions and testing functions are small. In our approach, the impedance matrix elements that correspond to these far interactions are set to zero after a threshold distance apart, typically in the order of one wavelength. This produces a sparse impedance matrix and the solution is known as partial matrix solver. It is possible to compute the solution iteratively, with successive increment of the threshold distance. The solution is said to have converged if the difference of the present solution and the previous is less than the pre-determined error threshold. However, with each successive increase in the threshold distance, additional impedance matrix elements need to be computed and stored. AIM is used to implement the partial matrix iterative solver. It is shown vi that after each iteration, there is no need to evaluate the new impedance matrix elements. There is only a need to allocate some additional grid nodes for the computation and the increase in memory storage is minimum. With specific placements of the nodal currents and the discrete Green’s function values on the grids, the potentials on the neighboring nodes outside the computation domain can be computed. This property enables the new scheme to be parallelized effectively to enable large microstrip circuit computation. A parallel ILU preconditioner is also formulated based on the properties of this new scheme. AIM has been reported to accelerate the computation of the TDIE using multiblock FFT algorithm. Due to the property of the lower triangular Toeplitz matrix, improvement to the computational scheme of the multi-block FFT algorithm has been proposed. The new scheme optimizes the performance by reducing the number of FFT transform of the aggregate current array to the spectral-frequency domain and the number of inverse FFT transform of the spectral-frequency domain transient fields. It is faster than the existing method of multi-level block FFT algorithm and offers greater flexibility and ease of implementation and allows caching of data onto secondary storage devices. Numerical results shows the improvement in the performance of the proposed method. vii List of Figures 2.1 2.2 2.3 A PEC object in an unbounded homogenous medium. . . . . . . . . A Rao-Wilton-Glisson (RWG) basis function. . . . . . . . . . . . . . The original RWG basis function and the grid current sources that has the same multipole moments about (xo , yo , zo ). . . . . . . . . . . . . . 2.4 Interpolation of the magnetic vector potentials to the vicinity of the centriods of the testing function. . . . . . . . . . . . . . . . . . . . . . 2.5 Computation of the curl of the nodal magnetic vector potentials using central difference numerical approximation. . . . . . . . . . . . . . . . 2.6 Bistatic RCS of a PEC sphere of 1m radius at 1.20GHz with 110454 basis functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Plot of the residual error with respect to the number of iterations using GMRES iterative solver and block preconditioner. . . . . . . . . . . . 2.8 Monostatic RCS of a meter NASA almond with 3510 unknowns at 757MHz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Monostatic RCS of a simplified aircraft model at 300MHz with 272760 triangular basis function computed using CFIE (α = 0.5) with GMRES solver and block preconditioner. . . . . . . . . . . . . . . . . . . . . . 2.10 Surface current density on the aircraft at 300MHz with vertical polarized plane wave incident at deg azimuth from the aircraft’s nose. . . 3.1 3.2 3.3 3.4 3.5 3.6 3.7 Supercomputer architecture. . . . . . . . . . . . . . . . . . . . . . . . Distributed parallel computing architecture. . . . . . . . . . . . . . . Potential of a source along uniform grid points. . . . . . . . . . . . . Interlaced grid system for FFT computation. . . . . . . . . . . . . . . Interpolated results of interlaced FFT results to obtain the final solution. Interlace scheme where the potentials are computed at 0.24λ grid. . . Computation of potentials for near field correction. (a) interlacing the ˆ direction (b) interlacing the grid in both the x ˆ and y ˆ grid in the x directions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 14 22 26 28 30 32 32 33 34 37 38 38 39 40 41 49 3.8 3.9 3.10 3.11 3.12 3.13 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 Comparison of the monostatic RCS of meter NASA almond at 757MHz with 3510 unknown basis functions computed using normal AIM and the parallelized interlaced FFT AIM scheme. . . . . . . . . . . . . . Comparison of the bistatic RCS of PEC sphere of diameter meter at 1.2GHz with 110454 unknown triangular basis functions computed using normal AIM and the parallelized interlaced grid AIM scheme. . Speedup ratio vs the number of distributed processors. . . . . . . . . Generic aircraft with tip to tail length of 14m, wingspan of 16m and a body height of 3.5m. . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of speed-up factors for the interlace FFT scheme vs parallel FFT scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bistatic RCS of the generic aircraft at 250MHz with a V-polarized electric field incident from the nose of the aircraft. . . . . . . . . . . . An arbitrary shaped scatterer embedded in layered dielectric medium . Two sheeted Riemann kz0 planes. . . . . . . . . . . . . . . . . . . . . Two sheeted Riemann kρ planes. . . . . . . . . . . . . . . . . . . . . Two sheeted Riemann kρ planes. . . . . . . . . . . . . . . . . . . . . An infinitely long microstrip transmission line. . . . . . . . . . . . . . Integration of Sommerfeld path for bound mode region of an infinitely long microstrip transmission line. . . . . . . . . . . . . . . . . . . . . Integration of Sommerfeld path for leaky modes in region of an infinitely long microstrip transmission line. . . . . . . . . . . . . . . . . Integration of Sommerfeld path for leaky mode in region of an infinitely long microstrip transmission line. . . . . . . . . . . . . . . . . Basis function to represent the longitudinal electric surface current densities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basis function to represent the transverse electric surface current densities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Iteration method used to find the propagation constant ky for the infinitely long planar transmission line at each frequency. . . . . . . . . Sommerfeld integration path for the multi-layer Dyadic Green’s function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sommerfeld integration path for the multi-layer Dyadic Green’s function in the kzm plane. . . . . . . . . . . . . . . . . . . . . . . . . . . . The contour C on the complex kρ plane for extracting the surface wave poles and residues using GPOF . . . . . . . . . . . . . . . . . . . . . The pole extraction and residue computation algorithm flow chart . A five layered grounded dielectric medium used as test case to verify the DCIM results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 50 50 51 51 52 52 57 62 63 64 66 67 68 69 71 72 76 78 81 85 86 88 281 [98] C. A. Balanis. Antenna Theory: Analysis and Design. Wiley, New York, second edition, 1997. [99] A. Taflove. Computational Electrodynamics: The finite difference time domain method. Norwood, MA: Artech House, 1995. [100] K.S. Kunz and R.J. Luebbers. 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Instead, we will list out the final formulation in this report for the completeness of representation. The mixed potential integral equation MPIE equation for planar multi-layer medium may be written as n ˆ × Einc jωAmi (r) + ∇φmi (r) = n m (r), ˆ× n (A.1) i=1 where the source point is at ith layer and the observation point is in the mth layer and mi Ami (r) = si φmi (r) = si KA (r|r ) · J(r )dS , (A.2) Kφmi (r|r ) · q(r )dS . (A.3) The dyadic kernel for a source in the mth region due to a source in the ith region in the mixed potential form, formulation C, is given as   mi mi Kxz Kxx   mi mi  mi KA =   Kyy Kyz  mi mi mi Kzz Kzy Kzy (A.4) where the individual components are expressed as mi mi = = Kyy Kxx S0 (GVmih ) 2πjω 285 (A.5) 286 z z=z1 z2 zi-1 zi zi+1 zn-1 zn Figure A.1: An arbitrary shaped scatterer embedded in layered dielectric medium . mi Kxz = −µi k2 Vh Ve cos ζS1 (Wmi − 2m Wmi ) 2πjωεm kρ kzm (A.6) mi Kyz = −µi k2 Vh Ve sin ζS1 (Wmi − 2m Wmi ) 2πjωεm kρ kzm (A.7) km Ih Ie cos ζS1 2 (Wmi − Wmi ) =− 2πjω kρ kzm (A.8) 1 k2 Ih Ie ) − Wmi sin ζS1 2m (Wmi 2πjω kρ kzm (A.9) −µm k2 k2 e e h S0 GImi − i2 ( zm GImi − GImi ) 2πjωεi kρ km (A.10) mi Kzx mi Kzy =− mi = Kzz The scalar Green’s function is defined as Kφmi = jω S0 (GVmie − GVmih ) 2π kρ (A.11) 287 where WiiV ← − − → Γ i e−jkzi [(z + z ) − 2zi ] − Γ i−1 e−jkzi [2zi−1 −(z+z )] + ← −− → 2j Γ i Γ i−1 e−2jψi sin[kzi (z − z )] jkzi = 2Zi Di , m=i (A.12) V Imi = GVmi = jkzm V V Wmi = I , Zm mi − → − → Z i−1 GIii (zi−1 , z ) T Vmi (z), ← − ← − − Z i GIii (zi , z ) T Vmi (z), − → − → Z i−1 GVii (zi−1 , z ) T Vmi (z), ← − ← − − Z i GVii (zi , z ) T Vmi (z), m=i (A.13) i−1≥m≥1 n+1≥m≥i+1 i−1≥m≥1 n+1≥m≥i+1 GVii (z, z ) = Z2i [e−jkzi |z−z | + QVi (z, z )]  ← −  Γ i e−jkzi [(z+z )−2zi ] +   → − QVi (z, z ) = Γ i−1 e−jkzi [2zi−1 −(z+z )] +  Di  ← −− →  2Γ −j2ϕi cos[kzi (z − z )] i Γ i−1 e ← −− → Di = − Γ i Γ i−1 e−j2ϕi (A.14) (A.15) (A.16)        (A.17) (A.18) For notation simplicity, we have suppressed in eq.(A.12-A.17) the superscripts ”e” kzi i and ”h”. For superscript ’e’ and ’h’, we use Zie = ωε and Zih = ωµ respectively. kzi i ˜ I from W ˜ V and G ˜ I from G ˜ V by replacing in the latter Moreover, we can obtain W mi mi mi mi the characteristic impedances Zi by their reciprocals. The rest of the notations used in the equations are as shown: ϕi = kzi di di = zi−1 − zi − → − → Z k − Zk+1 Γk = − → Z k − Zk+1 − → − → +jZk tk Z k = Zk Z k−1− , → Zk +j Z k−1 tk+1 ← − ← − k+1 tk+1 Z k = Zk+1 Z k+1 +jZ ← − Zk+1 +j Z k+11 tk+1 − → Z = Z1 ← − Z n = Zn+1 kzi = ki2 − kρ2 . ← − ← − Z k − Zk Γk = ← − Z k − Zk (A.19) (A.20) k = 2, 3, . . . , n k = n − 1, n − 2, . . . , (A.21) tk = tan ψk − →V T mi (z) = − → e−jkzm (z−zm ) [1 + Γ m−1 e−j2kzm (zm−1 −z) ] − → 1+ Γ m−1 e−j2ψm − → i−2 (1+ Γ k )e−jψk+1 → −j2ψk+1 k=m 1+− Γ ke (A.22) 288 ← − T Vmi (z) = ← − e−jkzm (zm−1 −z) [1 + Γ m e−j2kzm (z−zm ) ] ← − 1+ Γ m e−j2ψm ← − m−1 (1+ Γ k )e−jψk ← − k=i+1 1+ Γ e−j2ψk k (A.23) The inverse Fourier transforms in eq(A.5)-eq(A.11)are denoted as ∞ Sn [G(kρ )] = g(kρ )Jn (kρ ρ)kρn+1 dkρ . (A.24) Appendix B Preconditioning of the MoM Matrices This section of the appendix serves to illustrate in greater detail of the types of preconditioners used in the various chapters of this report. Preconditioners are generally used to lower the condition number of a matrix, and hence accelerating the convergence rate of the solution of a matrix equation by iterative methods. Solving electromagnetic solutions using method of moment (MoM ) usually involves a matrix equation of the following form ZI = V, (B.1) where Z is the impedance matrix, I and V are the current and excitation vectors respectively. Solving the matrix equation by direct methods such as Gaussian elimination or LU decomposition method involves O(N ) operations, where N is the number of surface discretization. . Solution by iterative methods not compute the inverse of the matrix directly. Instead, based on the updates of the current vector I at each solution, the residual error is computed and a new search direction is computed which will give rise to the next better estimates of I in general. Iterative methods requires the evaluation of the impedance matrix and current vector multiply which involves O(N ) operations. However, it the condition number of the impedance matrix is small, I typically converges within a small number of iterations as compared to N . Hence, iterative methods may scale in the O(N ). In general, the preconditioner −1 M modifies the MoM matrix equation as follows −1 −1 M ZI = M V, 289 (B.2) 290 if M −1 −1 −1 = Z , then M Z −1 = I, where I is the identity matrix. In this case, the −1 solution be found in a single iteration. However in deriving M will need O(N ) operations and O(N ) storage space which defeats the purpose of using an iterative −1 solution solver. So in general, we choose M −1 ≈ Z . A good preconditioner is one that requires least operation counts to compute the inverse or approximate inverse of M and requires little storage space and when it multiplies with the impedance matrix, the condition number is greatly reduced. The various preconditioners used in this work are the diagonal preconditioner, the block preconditioner, the incomplete LU factorization ILU(0), the incomplete LU factorization with double truncations ILUT and the block ILU preconditioner. B.1 Diagonal Preconditioner Diagonal preconditioner is the easiest to implement. M = diag Z , (B.3) M only contains the diagonal elements of Z and has only N elements. The inverse of M is the reciprocal of each of the diagonal elements. Though simple to implement, its performance is not as good as the other preconditioners. B.2 Block Preconditioner The block preconditioner requires the object for analysis be subdivided into different regions as shown in fig B.1. The basis functions within each region are numbered consecutively. If we only compute the impedance matrix terms within each region only and ignore the interactions between elements from neighboring regions, we will end up with M being a block diagonal matrix as shown in fig B.2. The inverse of M maybe computed by inverting the small blocks of sub-matrices using direct method. This preconditioner is good for parallelization to run on parallel computing. However, since the neighboring regions interactions are not computed, this preconditioner may not perform as well as compared to other preconditioners 291 Figure B.1: Subdiving the object of analysis into different regions. M= Figure B.2: The structure of the block diagonal matrix of M that take into account the neighboring interactions. This preconditioner is good for circuit elements that can be group into regions with very little coupling between the elements from adjacent regions. B.3 Incomplete LU Decomposition method ILU(0) [1] The filling of the matrix elements for M is the same as for Z, with the exception that if the impedance matrix element Mij is below a threshold τ1 , then Mij is set to zero. Hence the overall M is a sparse matrix. LU decomposition is used to factorize M. 292 In the factorization of the row, only the non-zero elements are factorized and filled as shown by algorithm below. This will ensure that the resultant L and U are also matrices with the same sparsity. Since the full LU factorization is not implemented, the partial LU decomposition is said to be ’incomplete’. foreach i=2 to N foreach j=1 to i-1 and where Mij = Mij =Mij /Mjj ; foreach k=j+1 to N and where Mik = Mik =Mik - Mij Mj,k end end end Algorithm 1: Generic Incomplete LU Factorization ILU(0) In filling the ith row of M, if M ij Mii < τ1 , then we can set Mij = 0. τ is usually set to be between 0.01 and 0.10. The lower the value of τ , the more elements M will have and will require more storage space. Alternatively, we can set Mij = if the distance between the ith testing and j th basis function exceeds a certain distance, which is usually specified in terms of guided wavelengths in the medium. This approach is being applied in circuit simulations in this work as it is more intuitive to set Mij = because of weak coupling as the distance between the testing and basis function increases in the microstrip circuits. ILU(0) generally performs better than block preconditioners and diagonal preconditioners as it takes into account all the couplings. However, this generic algorithm is not robust. There are instances when the ILU factorization fails due to a near to zero diagonal elements. There are many improved variations of ILU factorizations. But this generic algorithm suffice for most of the computation works. B.4 ILUT The deficiency in the generic ILU(0) algorithm is that the incomplete LU are blind to numerical values because the elements that are dropped depends on the structure of M. This can cause some difficulties for realistic problems that arise in many 293 applications. An alternative to this method is to drop the elements in the Gaussian elimination process according to their magnitude rather than their locations. ILUT [1] is a simple strategy to this approach and is yet akin to the generic ILU(0) algorithm. The algorithm is described as follows foreach i=2 to N w := ai∗ ; foreach k=1 to i-1 and when wk = wk = wk /akk ; Apply a dropping rule to wj ; if wk = then w := w − wk uk∗ ; end end Apply dropping rule to w; li,j := wj for j = 1, · · · , i − elements of lower triangular matrix; ui,j := wj for j = i, · · · , n elements of upper triangular matrix; end Algorithm 2: Incomplete LU with double dropping strategy ILUT is very much similar to ILU(0) algorithm except that for the double dropping strategies. For ILUT, the dropping and fill-ins can occur at any position on the row during factorization. This is in contrast to the generic ILU(0) algorithm where the dropping strategy is only applied to non-zero patterns of M. In general, ILUT preconditioner gives better convergence for iterative solution solvers than ILU(0) algorithm. B.5 Block ILU One major difficulty associated with ILU factorization of the preconditioner for simulating electrically large structures or circuits is the need for considerable storage resource. In ILU factorization of ith row of M, there is a need to access the information of the preceding rows to i−1. Hence, it is difficult to cache the information onto secondary storages such as onto a hard drive to reduce the storage requirement of the primary storage without sacrificing the time spent on accessing the data repeatedly. A new block ILU method [128] has been proposed recently that enable the matrix M to be cache onto the secondary storage devices while factorization with minimum 294 overhead. The matrix M can be partitioned into blocks. The simplest being subblock matrix as shown in eq(B.4) M= P Q R S , (B.4) where P and S are square sub-matrices. The inverse can be expressed as   −1 P Q , M = R S with (B.5) −1 P = (P − QS R)−1 −1 Q = −P(QS ) (B.6) −1 R = −(S R)P S=S −1 −1 −1 + (S R)P(QS ) M is a sparse matrix and its elements are filled according to the threshold τ1 as discussed in the ILU(0) factorization. Finding the inverse of M by applying eq(B.6) directly will result in a full matrix that requires O(N ) storage space. Hence in order to preserve the sparsity, there is a need to further impose additional thresholds to drop the elements with small values in P, Q, R and S. In the computation, there is a need to invert the matrix S. Assuming S is small matrix block and its inverse, S −1 can be computed by direct methods. We shall impose the threshold τ2 for the −1 dropping strategy for S , i.e. Sij−1 will be set to zero if −1 −1 Sij −1 Smax (−1) −1 stored. Smax is the max value of S . We shall denote S < τ2 and will not be as the matrix of S after threshold τ2 has been applied to drop smaller elements. Subsequently, S ( −1) −1 is used in the evaluation of P, Q, R and S. After each evaluation, the same dropping threshold τ2 is applied to drop the small elements. We shall denote P , Q , R and S as the matrices of P, Q, R and S after dropping the small elements. In general, eq(B.6) can be applied recursively, as shown in fig B.3 for a × sub-block matrices. Stage computes the inverse of the sub-matrix M1 , where M1 = P1 Q1 R S1 . 295 −1 The the inverse of the sub-matrix M1 is computed via eq(B.6). Applying the threshold τ2 and dropping the small elements, the inverse of the sub-matrix is approximately   −1 P Q1 . M1 =  R S1 Stage of the computation computes the inverse of the sub-matrix M2 , where P2 Q2 M2 = R S2 . We can apply eq(B.6) to compute the inverse of M2 and applying the threshold to drop the small elements as   −1 M2 =  P Q2 . R S2 In the computation, we need to compute the inverse of S2 . However, −1 −1 S2 = M . −1 M1 has been computed in stage of the algorithm and can be used directly in the computation in this stage. This is similar for stage of the computation, where finding the inverse of the sub-matrix block M3 = P3 Q3 R S3 −1 involves the computation of the inverse of S3 . However, S3 −1 ≈ M2 , which was previously computed. Hence, we can apply the algorithm recursively for matrices that are segregated into smaller sub-block matrices. The sub-block matrices can be cached into the secondary storage and is only accessed and read into the primary storage when it is needed for the sub-block matrix multiplies. Hence, only a few subblock matrices at stored in the primary storage at any one time and this effectively reduces the memory resources required for the computation. 296 P1 Q1 R1 S1 Stage P2 Q2 R2 S2 Stage P3 Q3 R3 S3 Stage Figure B.3: Stages of computing the inverse of the preconditioner matrix using block ILU with × sub-matrix blocks. [...]... of electric field integral equation (EFIE ) and the magnetic field integral equation (MFIE ) EFIE and MFIE both suffer from internal resonance problems where the impedance matrices become singular Linearly combining EFIE and MFIE to obtain combined field integral equation (CFIE ) removes this problem and ensures that the solution converges at all frequencies The MoM [24] solution of the integral equations... spatial domain The surface wave pole extraction method is discussed The circuit parameter extraction for arbitrary n-port device using 3 point method is presented The dielectric and conductor loss is incorporated into the simulations AIM is applied to simulate Ku band power combiner circuits with conducting via holes and conducting plated through slots Partial iterative matrix solver is implemented using. .. finite difference time domain method [5, 6] and finite element method [7, 8, 9] are the most commonly used to solve many electromagnetics problems PDE solver requires the entire computation domain to be discretized and solved in order to obtain the solution of the fields This is in contrast to the boundary integral method, which only requires the surface of the object to be discretized The method of moments... been utilized to investigate different classes of electromagnetic scattering and circuit simulations [21, 22] Even with the emergence of the effective computational methods in electromagnetics, the computing power required cannot be satisfied by conventional, single processor computer architecture There is an ever increasing quest to decrease the solution time and to distribute the storage and computational... suitable for solving MoM solution using AIM for reasons that will soon be apparent 2.2 Solution using Method of Moments Given the integral equations and the boundary conditions, we can solve for the unknown surface fields Once the surface fields are known, the field everywhere can be calculated Unless the surfaces coincide with some curvilinear coordinate system, the integral equations in general do not... sources and Green’s function in fullwave AIM simulation 143 4.58 Implementation of PMS solver using AIM 145 4.59 Implementation of PMS solver using AIM with computation of the potentials at the neighboring nodes of the computational domain 147 4.60 Sub-division of the computational domain for parallel computation of the global nodal potentials using PMS solver and. .. matrix and current vector without explicitly forming the impedance matrix Iterative solvers are used to obtain the solution of the matrix equation The use of preconditioners to accelerate the solution convergence is discussed EFIE and MFIE both suffer from internal resonance problems where the impedance matrices become singular Linearly combining the EFIE and MFIE to obtain the combined field integral. .. this problem and ensures that the solution converges at all frequencies In solving for the solution of CFIE using AIM, a novel memory saving testing scheme is presented This new scheme permits the solving of CFIE with AIM using the same amount of memory resources as compared to the solution using EFIE, but with the advantage of faster solution convergence due to the fact that CFIE is an integral equation... effectiveness of the new solver In chapter 5, AIM is used to accelerate the computation of the time domain integral equation, TDIE TDIE formulation and marching-on -time (MOT ) scheme are introduced The multi-block FFT algorithm is discussed An alternative block aggregate matrix-vector multiply scheme is introduced The effectiveness of the new scheme is analyzed and compared against the performance multi-block... function in MPIE formulation GA vector potential Green’s function in MPIE formulation β0 propagation constant of microstrip transmission line ρ distance between the source and the observation point YTi E intrinsic admittance as seen by the transverse TE waves in the ith medium YTi M intrinsic admittance as seen by the transverse TM waves in the ith medium xix Chapter 1 Introduction 1.1 Background Electromagnetics . ELECTROMAGNETIC SIMULATIONS IN FREQUENCY AND TIME DOMAIN USING ADAPTIVE INTEGRAL METHOD NG TIONG HUAT (M. Eng, B. Eng. (Hons) NUS) A THESIS. this thesis is to investigate methods to improve the performances of the adaptive integral method (AIM ) for effective large scale simulations in both the frequency and the time domain. This is achieved. . . . . . . . . . . . . 152 5 Time Domain Integral Equation 196 5.1 Time Domain Integral Equation Formulation . . . . . . . . . . . . . . 198 5.2 Far Field Scattering . . . . . . . . . . . . .